Network-based methods for computational diagnostics by means of R

Laurin A.J. Mueller; Matthias Dehmer; Frank Emmert-Streib

doi:10.1007/978-3-7091-0947-2_11

Network-based methods for computational diagnostics by means of R

Laurin A.J. Mueller
, Matthias Dehmer^*
, Frank Emmert-Streib

^*Corresponding author for this work

Private University for Health Sciences, Medical Informatics and Technology
Queen's University Belfast

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Networks representing biomedical data have become a powerful approach in different research disciplines dealing with complex diseases. Also, R and Bioconductor have emerged as a standard research environment to investigate and analyze high-throughput data. Therefore, we present and discuss existing packages, available in R or Bioconductor, that provide methods for computational diagnostics by means of networks. In particular, we summarize packages to reconstruct and analyze networks from high-throughput data. Moreover, we discuss packages that provide comprehensive methods to visualize large-scale gene networks in order to support the field of computational diagnostics of complex diseases. The aim of this chapter is to support an interdisciplinary research community dealing with computational diagnostics to investigate novel hypothesis in a medical and clinical context to gain a better understanding of complex diseases.

Original language	English
Title of host publication	Computational Medicine
Subtitle of host publication	Tools and Challenges
Publisher	Springer-Verlag Wien
Pages	185-197
Number of pages	13
Volume	9783709109472
ISBN (Electronic)	9783709109472
ISBN (Print)	3709109469, 9783709109465
DOIs	https://doi.org/10.1007/978-3-7091-0947-2_11
Publication status	Published - 1 Mar 2014
Externally published	Yes

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10.1007/978-3-7091-0947-2_11

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title = "Network-based methods for computational diagnostics by means of R",

abstract = "Networks representing biomedical data have become a powerful approach in different research disciplines dealing with complex diseases. Also, R and Bioconductor have emerged as a standard research environment to investigate and analyze high-throughput data. Therefore, we present and discuss existing packages, available in R or Bioconductor, that provide methods for computational diagnostics by means of networks. In particular, we summarize packages to reconstruct and analyze networks from high-throughput data. Moreover, we discuss packages that provide comprehensive methods to visualize large-scale gene networks in order to support the field of computational diagnostics of complex diseases. The aim of this chapter is to support an interdisciplinary research community dealing with computational diagnostics to investigate novel hypothesis in a medical and clinical context to gain a better understanding of complex diseases.",

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AU - Dehmer, Matthias

AU - Emmert-Streib, Frank

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AB - Networks representing biomedical data have become a powerful approach in different research disciplines dealing with complex diseases. Also, R and Bioconductor have emerged as a standard research environment to investigate and analyze high-throughput data. Therefore, we present and discuss existing packages, available in R or Bioconductor, that provide methods for computational diagnostics by means of networks. In particular, we summarize packages to reconstruct and analyze networks from high-throughput data. Moreover, we discuss packages that provide comprehensive methods to visualize large-scale gene networks in order to support the field of computational diagnostics of complex diseases. The aim of this chapter is to support an interdisciplinary research community dealing with computational diagnostics to investigate novel hypothesis in a medical and clinical context to gain a better understanding of complex diseases.

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SP - 185

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BT - Computational Medicine

PB - Springer-Verlag Wien

ER -

Network-based methods for computational diagnostics by means of R

Abstract

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